Dynamic Neural Network-Based Integrated Learning Algorithm for Driver Behavior

Abstract

Vehicle driver's perception, judgment, decision and action towards traffic environment are usually uncertain and inconsistent during their driving processes. Thus, it is difficult to use the traditional driving decision model to accurately predict the driving behaviors under these circumstances. This paper proposes a DNNIA algorithm to describe driving behavior by dynamically integrating ANNs. Specifically, some ANNs are first trained to learn different kinds of driving behaviors based on sample data and small amounts of these ANNs with minimal generalization error E are then selected and integrated to predict the final driving behavior. The Lagrangian function method is used to resolve the coefficient ωi for optimal ensemble. Moreover, by introducing the idea of agent alliance, the study takes each individual ANN as an agent in the alliance and outputs the maximal value among all the weighted average outputs of the neuron in each individual ANN. The proposed method is evaluated on some benchmark datasets to show its effectiveness. In addition, the predicted driver's habitual behavior by DNNIA, such as braking pedal, consistently accord with that revealed by the sample data, which proves its practicality for real-world problems.